Ultrasound has been an important imaging technique for detecting breast tumors. As opposed to the conventional B-mode
image, the ultrasound elastography is a new technique for imaging the elasticity and applied to detect the stiffness
of tissues. The red region of color elastography indicates the soft tissue and the blue one indicates the hard tissue, and
the harder tissue usually is classified to malignancy. In this paper, we proposed a CAD system on elastography to
measure whether this system is effective and accurate to classify the tumor into benign and malignant. According to the
features of elasticity, the color elastography was transferred to HSV color space and extracted meaningful features from
hue images. Then the neural network was utilized in multiple features to distinguish tumors. In this experiment, there
are 180 pathology-proven cases including 113 benign and 67 malignant cases used to examine the classification. The
results of the proposed system showed an accuracy of 83.89%, a sensitivity of 85.07% and a specificity of 83.19%.
Compared with the physician's diagnosis, an accuracy of 78.33%, a sensitivity of 53.73% and a specificity of 92.92%,
the proposed CAD system had better performance. Moreover, the agreement of the proposed CAD system and the
physician's diagnosis was calculated by kappa statistics, the kappa 0.54 indicated there is a fair agreement of observers.